Optimization of Direct Laser Deposition Process for Shot Sleeves Used in Aluminium Diecasting

Beyond Repair: How Direct Laser Deposition (DLD) Optimizes Shot Sleeves for High-Pressure Die Casting

This technical summary is based on the academic paper "Optimization of Direct Laser Deposition Process for Shot Sleeves Used in Aluminium Diecasting" by Bibin Babu and M. Muthukumaran, published in the International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS) (2018).

Keywords

• Primary Keyword: Direct Laser Deposition (DLD) for Shot Sleeves
• Secondary Keywords: Shot Sleeve Repair, H13 Tool Steel, Additive Manufacturing in Die Casting, DLD Process Optimization, ANOVA, Die Casting Components

Executive Summary

• The Challenge: Shot sleeves in aluminum die casting are critical, high-wear components, and finding an economical and effective method to manufacture or repair them with optimal material properties is a significant industrial challenge.
• The Method: The study applied a Direct Laser Deposition (DLD) process, using H13 tool steel as the deposit material on a 316L stainless steel substrate, and employed a Design of Experiments (DOE) approach to systematically optimize seven key process parameters.
• The Key Breakthrough: Analysis of Variance (ANOVA) revealed that laser power, percentage overlap, and inner gas pressure are the most dominant factors, collectively accounting for over 68% of the influence on the final deposit quality.
• The Bottom Line: This research provides a validated statistical model for controlling the DLD process to produce shot sleeves with superior and predictable microstructural properties, paving the way for longer component life and reduced operational costs.

The Challenge: Why This Research Matters for HPDC Professionals

In the demanding environment of aluminum die casting, the shot sleeve is a critical component that channels molten metal into the die. It is subjected to intense thermal cycles, wear, and mechanical stress, leading to degradation and eventual failure. Replacing these components is costly, and traditional repair methods often fall short of restoring original performance.

Additive manufacturing technologies like Direct Laser Deposition (DLD)—also known as Direct Metal Deposition (DMD)—offer a promising solution for both repair and fabrication. However, the DLD process involves a complex interplay of variables. Without a systematic approach, achieving the desired microstructure—such as a fine, equiaxed grain structure for toughness and resistance to cracking—is a matter of trial and error. This research addresses the critical need for a structured methodology to optimize DLD parameters specifically for creating high-performance shot sleeves.

The Approach: Unpacking the Methodology

The researchers conducted a rigorous experimental study to identify the optimal settings for the DLD process. Their approach was designed to build confidence in the results by systematically evaluating the cause-and-effect relationships between process inputs and material outputs.

Method 1: Material and Process Selection
* Deposit Material: H13 Tool Steel was chosen for its inherent toughness, a critical property for shot sleeves.
* Substrate Material: 316L Stainless Steel was used as the base, valued for its corrosion resistance.
* Process: A DLD system was used to fuse H13 powder onto the 316L substrate layer by layer, creating a metallurgical bond.

Method 2: Design of Experiments (DOE)
* Control Factors: Seven key process parameters were identified as variables: Laser Power (watt), Spot Size (mm), Inner Gas Pressure (psi), Outer Gas Pressure (psi), Feedrate (ipm), Powder Flow Rate (g/min), and Percentage Overlap (%).
* Experimental Design: A Taguchi L18 Orthogonal Array was used. This statistical tool allows for an efficient evaluation of multiple factors with a minimal number of experimental runs (18 in this case), saving time and resources.
* Evaluation Criteria: The quality of the resulting deposit was measured against six critical criteria: Knoop hardness, dendrite size, secondary dendrite arm spacing (SDAS), ASTM grain size, build rate, and porosity. These were combined into a single "Overall Evaluation Criterion" (OEC) to simplify optimization.

The Breakthrough: Key Findings & Data

The study successfully moved beyond guesswork to provide a data-driven blueprint for DLD process control. The statistical analysis pinpointed which parameters matter most and validated the predictability of the process.

Finding 1: Laser Power, Overlap, and Gas Pressure are the Dominant Control Factors

The Analysis of Variance (ANOVA) quantified the influence of each parameter on the final material quality. The results were unequivocal: a few key factors drive the majority of the outcome. As shown in TABLE VII, the top three contributors to process variation were:
* Laser Power (P): 28.98% contribution
* Percentage Overlap (O): 21.10% contribution
* Inner Gas Pressure (IG): 17.43% contribution

Together, these three parameters account for 67.51% of the total influence, making them the primary levers for process engineers to adjust when targeting specific microstructural properties.

Finding 2: The Statistical Model Accurately Predicts Optimal Performance

After identifying the optimal levels for each parameter based on the experimental data, the researchers conducted a confirmation experiment using these settings. The results were a strong validation of the methodology. The predicted value for the mean performance was 78.64, and the confirmation experiment yielded a value of 77.19. Similarly, the predicted S/N ratio (a measure of robustness) was 43.87, while the experimental result was 42.52. Because the experimental results fell well within the calculated confidence intervals, the study demonstrated that the process is not only optimizable but also predictable and controllable.

Practical Implications for R&D and Operations

• For Process Engineers: This study suggests that adjusting laser power, percentage overlap, and inner gas pressure may contribute to precisely controlling the microstructure and mechanical properties of repaired or fabricated shot sleeves, reducing defects like porosity and cracking.
• For Quality Control Teams: The data in TABLE V and TABLE VII of the paper illustrates the effect of specific process parameters on key properties like hardness, grain size, and SDAS, which could inform new quality inspection criteria for additively manufactured components.
• For Design Engineers: The findings indicate that the interaction between the DLD process and the substrate material could influence residual stress and defect formation, suggesting this is a valuable consideration when designing components for additive repair or manufacturing.

Paper Details

Optimization of Direct Laser Deposition Process for Shot Sleeves Used in Aluminium Diecasting

1. Overview:

• Title: Optimization of Direct Laser Deposition Process for Shot Sleeves Used in Aluminium Diecasting
• Author: Bibin Babu, M. Muthukumaran
• Year of publication: 2018
• Journal/academic society of publication: International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS)
• Keywords: Direct Laser Deposition Process, Die casting, Rapid prototyping, Tool Steels, ANOVA

2. Abstract:

Optimization is a process or a methodology of making something a fully functional, perfect, effective as possible. In product design optimization of the design is the most economical and creating efficient design. By using product models either by hand or through several different software programs optimal product can be done. In this paper a Direct Laser Deposition (DLD) method is applied to create shot sleeve prototype. A laser beam is used to fuse metal powder onto a substrate in the form of many layers and the part is gradually fabricated to near net-shape. The shot sleeves used in aluminium die casting produced by laser deposition method is optimized. Shot sleeves are critical elements for aluminium die casting through which the molten medium is transferred into the die. H13 Tool steel is used as the deposit material and the 316L stainless steel as the substrate material. The micro structural properties like the knoop hardness, dendrite size, secondary dendrite arm spacing (SDAS) ASTM grain size, the build rate and the porosity are used as the evaluation criteria. The experiment is conducted in random order and the analysis are obtained by minitab software.

3. Introduction:

In recent years, additive manufacturing processes, characterized by layer-upon-layer construction, have emerged as an alternative to conventional manufacturing for materials like steel, aluminum, and titanium alloys. Direct metal deposition (DMD) is an advanced additive technology with increasing interest for maintenance, repair, and overhaul of critical, high-cost products, such as those in the aerospace and automotive industry. This paper discusses the application of Direct Laser Deposition (DLD), a laser-assisted process, for creating and optimizing shot sleeves used in aluminum die casting. The process utilizes a computer-controlled laser to weld streams of metallic powders into custom parts, offering advantages like minimal distortion, reduced heat-affected zones, and superior surface quality.

4. Summary of the study:

Background of the research topic:

The study is situated within the field of additive manufacturing, specifically focusing on the Direct Laser Deposition (DLD) process for high-value industrial components. It addresses the need for efficient and high-quality repair and fabrication methods for parts like die casting shot sleeves, which are subject to severe operating conditions.

Status of previous research:

The paper acknowledges the advancements in rapid prototyping (RP) technologies, enabled by developments in computer-related fields like CAD, CAM, and CNC machining. It describes the fundamental mechanism of DLD, where a laser creates a melt pool on a substrate while metallic powder is simultaneously fed, resulting in a metallurgically bonded deposit.

Purpose of the study:

The primary purpose is to conduct a Design of Experiments (DOE) to optimize the DLD process for depositing H13 tool steel onto a 316L stainless steel substrate. The goal is to determine the ideal combination of process parameters to achieve superior microstructural properties (e.g., fine grain size, high hardness, low porosity) suitable for shot sleeve applications.

Core study:

The core of the study is an experimental investigation using a Taguchi L18 orthogonal array to analyze the effects of seven DLD process parameters on six distinct quality characteristics. These multiple responses are consolidated into a single Overall Evaluation Criterion (OEC) to facilitate a holistic optimization. The significance of each parameter is quantified using Analysis of Variance (ANOVA), and a predictive model for optimal performance is developed and subsequently verified through a confirmation experiment.

5. Research Methodology

Research Design:

The study employs a formal Design of Experiments (DOE) methodology based on the Taguchi method. An L18 orthogonal array was selected to systematically investigate seven control factors: laser power, spot size, inner gas pressure, outer gas pressure, feedrate, powder flow rate, and percentage overlap.

Data Collection and Analysis Methods:

Samples were fabricated according to the L18 array, with each experiment repeated three times. The fabricated samples were sectioned, mounted, polished, and etched for microstructural analysis. Data was collected for six response variables: build rate, Knoop hardness, SDAS, ASTM grain size, cracks, and porosity. The collected data was analyzed using Minitab software, employing Analysis of the Mean (ANOM) and Analysis of Variance (ANOVA) to determine the significance of each factor and predict optimal settings.

Research Topics and Scope:

The research is focused on the optimization of the DLD process for a specific material combination (H13 tool steel on 316L stainless steel) relevant to die casting applications. The scope is limited to the seven selected process parameters and their impact on the six specified microstructural and physical properties of the deposited material.

6. Key Results:

Key Results:

• The Analysis of Variance (ANOVA) identified the most statistically significant process parameters affecting the overall quality of the deposition.
• Laser power was the most influential factor, contributing 28.98% to the process variation.
• Percentage overlap was the second most significant factor, with a contribution of 21.10%.
• Inner gas pressure was the third most significant, contributing 17.43%.
• The combined effect of laser power, overlap, and inner gas pressure accounts for over 67% of the total process influence.
• A confirmation experiment was conducted using the optimal parameter levels predicted by the statistical model. The experimental results for the mean (77.19) and S/N ratio (42.52) fell within the predicted confidence intervals, successfully validating the optimization model.

Figure Name List:

• Fig. 1 Main components of laser deposition line
• Fig. 2 Percentage contribution of factors

7. Conclusion:

The laser deposition process was successfully optimized for build rate, micro-hardness, porosity, SDAS, and ASTM grain size. The optimal parameter combinations were determined for the defined Overall Evaluation Criterion (OEC). The study successfully developed a predictive model, and the optimal values were verified through confirmation experiments, which fell within the estimated confidence interval limits. The results demonstrate that the DLD system was made robust and controllable against the influence of interactions, noise, and experimental errors, and the resulting microstructure was studied and validated.

8. References:

• [1]. El Cheikh, H.; Courant, B.; Hascoet, J.; Guillen, R., (2012) Prediction and analytical description of the single laser track geometry in direct laser fabrication from process parameters and energy balance reasoning, Journal of Materials Process Technology, 212, 1832-1839.
• [2]. Gao, R.; Wang, L.; Teti, R.; Dornfeld, D.; Kumara, S.; Mori, M.; Helu, M. (2015) Cloud-enabled Prognosis for Manufacturing, CIRP Ann. Manufacturing Technology, 64, 749-772.
• [3]. Riveiro, A.; Mejías, A.; Lusquiños, F.; del Val, J.; Comesaña, R.; Pardo, J.; Pou, J, (2014) Laser cladding of aluminium on AlSi 304 stainless steel with high-power diode lasers, Surface Coating Technology, 253, 214-220.
• [4]. Ocylok, S.; Alexeev, E.; Mann, S.; Weisheit, A.; Wissenbach, K.; Kelbassa, I, (2014) Correlations of Melt Pool Geometry and Process Parameters during Laser Metal Deposition by Coaxial Process Monitoring, Physics Procedia, 56, 228-238.
• [5]. Attar, H.; Ehtemam-Haghighi, S.; Kent, D.; Wu, X.; Dargusch, M.S, (2017) Comparative study of commercially pure titanium produced by laser engineered net shaping, selective laser melting and casting processes, Material Science Engineering, 705, 385-393.
• [6]. Angelastro, A.; Campanelli, S.L.; Casalino, G, (2017) Statistical analysis and optimization of direct metal laser deposition of 227-F Colmonoy nickel alloy, Optical Laser Technology, 94, 138-145
• [7]. Dutta, B., Palaniswamy, S., Choi, J., Song, L.J., Mazumder, J., (2011) Additive manufacturing by direct metal deposition, Advanced Materials & Processing, Vol.169 (5). Pp. 33-36
• [8]. Leyens, C., Beyer, E., (2015) Innovations in laser cladding and direct laser metal deposition. Laser Surface Engineering, Pp. 181-192.
• [9]. Li, S., Wei, Q., Shi, Y., Zhu, Z., Zhang, D., (2015) Microstructure Characteristics of Inconel 625 Superalloy Manufactured by Selective Laser Melting, Journal of Materials Science & Technology, Vol.31, Iss. 9, Pp.946-952.
• [10]. Wilson, J.M., Piya, C., Shin, Y.C., Zhao, F., Ramani, K., (2014) Remanufacturing of turbine blades by laser direct deposition with its energy and environmental impact analysis, Journal of Cleaner Production, Vol. 80, 1 Pp. 170-178.
• [11]. Fearon, E. and Watkins, K. (2004) Optimization of Layer Height Control in Direct Laser Deposition, 23rd International Congress on Applications of Laser & Electro-optics paper No. 1708.
• [12]. Peng, L., Taiping, Y., Sheng, L., Dongsheng, L. Qianwu, H., Weihao, X., and Xiaoyan, Z. (2005) Direct Laser Fabrication of Nickel Alloy Samples Internaitonal Journal of Machine Tools and Manufacture" 45:1288-1294
• [13]. Prakash .S (2004) Optimization of laser aided manufacturing process using the design of experiments approach-Taguchi way, Thesis",,University of Missouri-Rolla 8431.

Expert Q&A: Your Top Questions Answered

Q1: Why was H13 tool steel chosen for the deposit material and 316L stainless steel for the substrate?

A1: The paper selected these materials to simulate a common industrial scenario for component repair or enhancement. H13 tool steel is known for its inherent toughness and wear resistance at high temperatures, making it an ideal choice for the working surface of a shot sleeve. 316L stainless steel was chosen as the substrate for its excellent corrosion resistance, representing the base structure of the component. This combination allows the study to evaluate the metallurgical bond and resulting properties between two dissimilar but industrially relevant metals.

Q2: What is the significance of using a Taguchi L18 Orthogonal Array for this experiment?

A2: The L18 Orthogonal Array is a powerful statistical tool for Design of Experiments (DOE). Its primary advantage is efficiency. Instead of testing every possible combination of the seven factors and their levels (which would require hundreds of experiments), the L18 array allows researchers to gather reliable data on the main effects of each factor with only 18 experimental runs. This drastically reduces the time, material, and cost required for the optimization study while still providing robust insights into the process.

Q3: The paper combines multiple quality characteristics into a single Overall Evaluation Criterion (OEC). What is the advantage of this approach?

A3: Optimizing for six different outputs simultaneously (e.g., maximizing hardness while minimizing porosity) is extremely complex. The OEC simplifies this by converting the multiple quality criteria into a single, weighted score. As described in the "Formulation The Overall Evaluation Criterion" section, this method assigns a value to each response based on whether a higher or lower value is better, allowing the researchers to find a single set of "best" parameters that provides a balanced and globally optimal result across all criteria.

Q4: Which process parameters had the most significant impact on the final quality of the shot sleeve prototype?

A4: The Analysis of Variance (ANOVA), detailed in TABLE VII, clearly quantifies the influence of each factor. The three most dominant parameters were Laser Power (28.98% contribution), Percentage Overlap (21.10% contribution), and Inner Gas Pressure (17.43% contribution). These three factors alone account for over two-thirds of the total variation in the process, making them the most critical parameters to control for achieving desired results.

Q5: What does a smaller Secondary Dendrite Arm Spacing (SDAS) indicate about the material quality?

A5: As stated in the "Responses" section, a smaller or finer SDAS is desirable because it indicates a faster cooling rate during solidification of the molten metal. This rapid cooling leads to a finer microstructure, which generally improves material properties. Specifically, a fine SDAS helps reduce inter-dendritic shrinkage and porosity, avoids the formation of large, brittle phases (like Fe3C in steel), and ultimately increases the overall strength and toughness of the deposited material.

Conclusion: Paving the Way for Higher Quality and Productivity

The challenge of manufacturing and repairing high-wear components like shot sleeves requires a precise and repeatable process. This study demonstrates that Direct Laser Deposition (DLD) for Shot Sleeves is not only a viable advanced manufacturing technique but also a highly controllable one. By systematically identifying the dominant process variables—laser power, overlap, and inner gas pressure—this research provides a clear roadmap for engineers to move from trial-and-error to data-driven optimization. The validated model offers a powerful tool for predicting and achieving superior microstructural properties, leading directly to more durable components, reduced downtime, and lower operational costs in die casting facilities.

At CASTMAN, we are committed to applying the latest industry research to help our customers achieve higher productivity and quality. If the challenges discussed in this paper align with your operational goals, contact our engineering team to explore how these principles can be implemented in your components.

Copyright Information

This content is a summary and analysis based on the paper "Optimization of Direct Laser Deposition Process for Shot Sleeves Used in Aluminium Diecasting" by "Bibin Babu and M. Muthukumaran".

Source: The paper was published in the International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS), Volume VII, Issue V, May 2018. A direct link is not provided in the source document, but it can be found by searching for the title and journal.

This material is for informational purposes only. Unauthorized commercial use is prohibited.
Copyright © 2025 CASTMAN. All rights reserved.